Auto Focusing Method of Imaging System of Digital PCR Instrument Based on BP Neural Network

2019 ◽  
Vol 33 (12) ◽  
pp. 1954031 ◽  
Author(s):  
Shanxiong Chen ◽  
Xueqing Xie ◽  
Fangyuan Zheng ◽  
Sheng Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Good Accuracy ◽  
Imaging System ◽  
Digital Pcr ◽  
Imaging Device ◽  
Focusing Effect ◽  
Electronic Imaging ◽  
Som Neural Network ◽  
Auto Focusing

The digital PCR instrument is a digital instrument for amplifying specific DNA fragments. The problem studied in this paper is the autofocus problem of its electronic imaging device. Based on the analysis of existing SOM neural network autofocus scheme, we propose an improved scheme-BP neural network for autofocus. It directly takes the SOM input and the actual focus position as the input and output of the BP neural network, which eliminates the process of prior classification and then corresponding to the focus matrix in the original SOM scheme, saving time. The experimental results show that the traditional autofocus method has good focusing effect, but the speed is slow, and the universality of the BP neural network autofocus scheme is not good enough, but within a good accuracy range, the speed is faster. Compared to traditional focusing methods, the autofocus scheme designed in this paper successfully achieves faster focusing speed for biochips.

scholarly journals Position Measurement Based on Fisheye Imaging

Proceedings ◽  
2019 ◽  
Vol 15 (1) ◽  
pp. 38
Author(s):  
Xianjing Li ◽  
Kun Li ◽  
Yanwen Chen ◽  
ZhongHao Li ◽  
Yan Han
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Imaging System ◽  
Large Angle ◽  
Image Features ◽  
Position Measurement ◽  
Fisheye Lens ◽  
Position Information ◽  
Object Distance ◽  
Nonlinear Imaging

For the omnidirectional measurement, the collected images of large-angle fisheye lens need to be corrected and spliced before next procedure, which is complicated and inaccurate. In this paper, a direct position measurement method based on fisheye imaging is proposed for large-angle imaging without any image correcting and splicing. A nonlinear imaging system of fisheye lens is used to acquire the sequence images based on its distortion model, and the critical distortion features of the sequence images are extracted, which contains the position information. And a BP neural network is trained with the extracted image features of previous standard experimental dataset. Finally, the trained BP neural network is employed to measure the object’s distance. Experimental results demonstrate show that the proposed method achieves simple close-object distance measurement with high robustness and a measurement error of ±0.5cm. The proposed method overcomes the shortcomings of conventional measurement methods and expands the fisheye applications filed for omnidirectional measurement.

scholarly journals Stereo Imaging Using Hardwired Self-Organizing Object Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5833
Author(s):  
Ching-Han Chen ◽  
Guan-Wei Lan ◽  
Ching-Yi Chen ◽  
Yen-Hsiang Huang
Keyword(s):  
Neural Network ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Imaging System ◽  
Object Segmentation ◽  
Hierarchical Architecture ◽  
Absolute Difference ◽  
Stereo Imaging ◽  
Som Neural Network ◽  
Self Organizing

Stereo vision utilizes two cameras to acquire two respective images, and then determines the depth map by calculating the disparity between two images. In general, object segmentation and stereo matching are some of the important technologies that are often used in establishing stereo vision systems. In this study, we implement a highly efficient self-organizing map (SOM) neural network hardware accelerator as unsupervised color segmentation for real-time stereo imaging. The stereo imaging system is established by pipelined, hierarchical architecture, which includes an SOM neural network module, a connected component labeling module, and a sum-of-absolute-difference-based stereo matching module. The experiment is conducted on a hardware resources-constrained embedded system. The performance of stereo imaging system is able to achieve 13.8 frames per second of 640 × 480 resolution color images.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

Sign in / Sign up

Export Citation Format

Share Document